Box S3 of File S1, shows how to extract estimates, predictions, and samples from the fitted model.
This paper presents the NN methodology used in developing the models, the accuracy of the predictions, and some sample results.
This paper presents the NN methodology used in developing the appliances, lighting, and space-cooling component of the model, the accuracy of its predictions, and some sample results.
Adaptive Rapid Environmental Assessment (AREA) is an adaptive sampling concept being developed in connection with the emergence of Autonomous Ocean Sampling Networks and interdisciplinary ensemble predictions and adaptive sampling via Error Subspace Statistical Estimation (ESSE).
In contrast, while randomized controlled studies are too small in size to statistically verify efficacy, they can provide feasibility information (outcome predictions, protocol adherence and sample size determination), which is useful in the design of full randomized controlled trials.
However, few studies have attempted a systematic analysis of the benefits of such stratification, especially in combination with other factors such as sensor type, statistical prediction method and sampling design of the reference inventory data.
The resolution of Eq. 1 is done in three steps: prediction, update, and sampling.
Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach.
For most instances, there was only one classifier reported a positive prediction and the sample was categorized into the corresponding subtype.
Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes.
